Wide ResNet-18. Previous work indicates that data augmentation has a positive effect on performance for this dataset [1]}. Fig. REF  (left) confirms that mixup and manifold mixup improve the generalization performance on clean data and highlights the advantage of data augmentation. The NFM training scheme is also capable of further improving the generalization performance. In addition, we see that the model trained with NFM is less sensitive to both white noise and salt and pepper perturbations. These results are surprising, as robustness is often thought to be at odds with accuracy [2]}. However, we demonstrate NFM has the ability to improve both accuracy and robustness.
Table REF  indicates that for the same \(\alpha \) , NFM can achieve an average test accuracy of \(80.9\%\)  compared to only \(80.3\%\)  in the mixup setting.
